TY  - GEN
A1  - Bartz, Christian
A1  - Yang, Haojin
A1  - Bethge, Joseph
A1  - Meinel, Christoph
T1  - LoANs
BT  - Weakly Supervised Object Detection with Localizer Assessor Networks
T2  - Computer Vision – ACCV 2018 Workshops
N2  - Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.
Y1  - 2019
SN  - 978-3-030-21074-8
SN  - 978-3-030-21073-1
U6  - https://doi.org/10.1007/978-3-030-21074-8_29
SN  - 0302-9743
SN  - 1611-3349
VL  - 11367
SP  - 341
EP  - 356
PB  - Springer
CY  - Cham
ER  -